Finding the Best Match - a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions.

ABSTRACT: With the need for psychological help long exceeding the supply, finding ways of scaling, and better allocating mental health support is a necessity. This paper contributes by investigating how to best predict intervention dropout and failure to allow for a need-based adaptation of treatment. We systematically compare the predictive power of different text representation methods (metadata, TF-IDF, sentiment and topic analysis, and word embeddings) in combination with supplementary numerical inputs (socio-demographic, evaluation, and closed-question data). Additionally, we address the research gap of which ML model types - ranging from linear to sophisticated deep learning models - are best suited for different features and outcome variables. To this end, we analyze nearly 16.000 open-text a

SUBMITTER: Zantvoort K 

PROVIDER: S-EPMC10620349 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature